Technology evaluation and uncertainty-based design optimization

Abstract

Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.